import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import *
import numpy as np
from typing import Dict, List, Tuple

class TemporalConvolutionalNetwork(tf.keras.layers.Layer):
    """
    Temporal Convolutional Network for sequence modeling
    """
    def __init__(self, filters, kernel_size, dilation_rates, dropout_rate=0.2):
        super().__init__()
        self.convs = []
        self.norms = []
        self.dropouts = []
        
        for dilation_rate in dilation_rates:
            self.convs.append(
                Conv1D(filters, kernel_size, dilation_rate=dilation_rate, 
                      padding='causal', activation='relu')
            )
            self.norms.append(LayerNormalization())
            self.dropouts.append(Dropout(dropout_rate))
    
    def call(self, x, training=None):
        for conv, norm, dropout in zip(self.convs, self.norms, self.dropouts):
            residual = x
            x = conv(x)
            x = norm(x)
            x = dropout(x, training=training)
            
            # Residual connection if dimensions match
            if residual.shape[-1] == x.shape[-1]:
                x = Add()([x, residual])
        return x

class MultiScaleAttention(tf.keras.layers.Layer):
    """
    Multi-scale attention mechanism for different time horizons
    """
    def __init__(self, d_model, num_heads_list=[2, 4, 8]):
        super().__init__()
        self.d_model = d_model
        self.attention_layers = []
        self.scale_projections = []
        
        for num_heads in num_heads_list:
            self.attention_layers.append(
                MultiHeadAttention(num_heads=num_heads, key_dim=d_model//num_heads)
            )
            self.scale_projections.append(Dense(d_model))
        
        self.combine_layer = Dense(d_model)
    
    def call(self, x):
        attention_outputs = []
        
        for attention_layer, projection in zip(self.attention_layers, self.scale_projections):
            att_out = attention_layer(x, x)
            proj_out = projection(att_out)
            attention_outputs.append(proj_out)
        
        # Combine multi-scale attention
        combined = Add()(attention_outputs)
        return self.combine_layer(combined)

class AdaptiveFeatureFusion(tf.keras.layers.Layer):
    """
    Adaptive feature fusion with learned importance weights
    """
    def __init__(self, num_features):
        super().__init__()
        self.num_features = num_features
        self.importance_network = Sequential([
            Dense(64, activation='relu'),
            Dense(32, activation='relu'),
            Dense(num_features, activation='softmax')
        ])
    
    def call(self, features_list):
        # Stack features
        stacked = tf.stack(features_list, axis=-1)
        
        # Learn importance weights
        importance_weights = self.importance_network(
            tf.reduce_mean(stacked, axis=[1, 2])
        )
        
        # Weighted combination
        weighted = tf.reduce_sum(
            stacked * tf.expand_dims(tf.expand_dims(importance_weights, 1), 1), 
            axis=-1
        )
        return weighted

class NextGenEnsembleModel:
    """
    Next-generation ensemble model with advanced architectures
    """
    def __init__(self, sequence_length=60, n_features=100):
        self.sequence_length = sequence_length
        self.n_features = n_features
        
    def build_advanced_ensemble(self):
        inputs = Input(shape=(self.sequence_length, self.n_features))
        
        # 1. Temporal Convolutional Branch
        tcn_out = TemporalConvolutionalNetwork(
            filters=64, kernel_size=3, 
            dilation_rates=[1, 2, 4, 8, 16]
        )(inputs)
        tcn_pred = GlobalAveragePooling1D()(tcn_out)
        tcn_pred = Dense(1, name='tcn_prediction')(tcn_pred)
        
        # 2. Multi-Scale Transformer Branch
        transformer_out = MultiScaleAttention(d_model=64)(inputs)
        transformer_pred = GlobalAveragePooling1D()(transformer_out)
        transformer_pred = Dense(1, name='transformer_prediction')(transformer_pred)
        
        # 3. Wavelet-Enhanced LSTM Branch
        # Wavelet decomposition simulation
        wavelet_features = Dense(64, activation='tanh')(inputs)
        lstm_out = LSTM(64, return_sequences=True)(wavelet_features)
        lstm_out = LSTM(32)(lstm_out)
        lstm_pred = Dense(1, name='lstm_prediction')(lstm_out)
        
        # 4. Graph Neural Network Branch (for market relationships)
        # Simplified GNN implementation
        graph_features = Dense(64, activation='relu')(inputs)
        graph_attention = MultiHeadAttention(num_heads=4, key_dim=16)(
            graph_features, graph_features
        )
        graph_pred = GlobalAveragePooling1D()(graph_attention)
        graph_pred = Dense(1, name='graph_prediction')(graph_pred)
        
        # Adaptive Feature Fusion
        fusion_layer = AdaptiveFeatureFusion(4)
        fused_features = fusion_layer([tcn_pred, transformer_pred, lstm_pred, graph_pred])
        
        # Meta-learner with uncertainty quantification
        meta_features = Concatenate()([tcn_pred, transformer_pred, lstm_pred, graph_pred])
        meta_dense = Dense(32, activation='relu')(meta_features)
        meta_dense = Dropout(0.3)(meta_dense)
        
        # Main prediction
        main_prediction = Dense(1, name='main_prediction')(meta_dense)
        
        # Uncertainty estimation
        uncertainty = Dense(1, activation='softplus', name='uncertainty')(meta_dense)
        
        # Market regime classification
        regime_classifier = Dense(5, activation='softmax', name='regime_prediction')(meta_dense)
        
        model = Model(
            inputs=inputs,
            outputs=[main_prediction, uncertainty, regime_classifier]
        )
        
        return model

class AdvancedTrainingSystem:
    """
    Advanced training system with multiple optimization strategies
    """
    def __init__(self, model):
        self.model = model
        self.training_history = {}
        
    def curriculum_learning_schedule(self, epoch, logs=None):
        """Implement curriculum learning"""
        if epoch < 20:
            # Easy samples first (stable market periods)
            difficulty_threshold = 0.1
        elif epoch < 50:
            # Medium difficulty
            difficulty_threshold = 0.3
        else:
            # All samples
            difficulty_threshold = 1.0
        
        return difficulty_threshold
    
    def progressive_unfreezing(self, epoch):
        """Progressive unfreezing of model layers"""
        total_layers = len(self.model.layers)
        unfreeze_rate = epoch / 100  # Unfreeze over 100 epochs
        
        layers_to_unfreeze = int(total_layers * unfreeze_rate)
        
        for i, layer in enumerate(self.model.layers):
            layer.trainable = i < layers_to_unfreeze
    
    def adaptive_loss_weighting(self, epoch, logs=None):
        """Adaptive loss weighting based on performance"""
        if logs is None:
            return [1.0, 0.1, 0.3]  # Default weights
        
        # Adjust weights based on validation performance
        val_loss = logs.get('val_loss', 1.0)
        uncertainty_loss = logs.get('val_uncertainty_loss', 0.1)
        regime_loss = logs.get('val_regime_prediction_loss', 0.3)
        
        # Normalize and adjust
        total_loss = val_loss + uncertainty_loss + regime_loss
        weights = [
            val_loss / total_loss,
            uncertainty_loss / total_loss * 0.5,
            regime_loss / total_loss * 0.8
        ]
        
        return weights
    
    def train_with_advanced_techniques(self, X_train, y_train, X_val, y_val, epochs=100):
        """Train with advanced optimization techniques"""
        
        # Custom loss function with uncertainty
        def uncertainty_weighted_loss(y_true, y_pred):
            main_pred = y_pred[:, 0:1]
            uncertainty = y_pred[:, 1:2]
            
            mse = tf.square(y_true - main_pred)
            return tf.reduce_mean(mse / (uncertainty + 1e-8) + tf.log(uncertainty + 1e-8))
        
        # Compile with advanced optimizer
        optimizer = tf.keras.optimizers.AdamW(
            learning_rate=0.001,
            weight_decay=1e-5,
            beta_1=0.9,
            beta_2=0.999
        )
        
        self.model.compile(
            optimizer=optimizer,
            loss={
                'main_prediction': uncertainty_weighted_loss,
                'uncertainty': 'mae',
                'regime_prediction': 'categorical_crossentropy'
            },
            loss_weights=[1.0, 0.1, 0.3]
        )
        
        # Advanced callbacks
        callbacks = [
            tf.keras.callbacks.ReduceLROnPlateau(
                monitor='val_loss', factor=0.5, patience=10, min_lr=1e-7
            ),
            tf.keras.callbacks.EarlyStopping(
                monitor='val_loss', patience=25, restore_best_weights=True
            ),
            tf.keras.callbacks.LambdaCallback(
                on_epoch_end=lambda epoch, logs: self.progressive_unfreezing(epoch)
            )
        ]
        
        history = self.model.fit(
            X_train, y_train,
            validation_data=(X_val, y_val),
            epochs=epochs,
            batch_size=32,
            callbacks=callbacks,
            verbose=1
        )
        
        return history